AUTHORS: Alessandro Pellegrini, Pierangelo Di Sanzo

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ABSTRACT: In this paper, we target collaborative kerbside collection from a planning and real-time monitoring point of view. This is a non-trivial problem, where several vehicles are set on streets to finish a task—the collection of all waste—by a certain maximum amount of time. While deciding upon a collaborative strategy is a well-studied and complex problem by itself, we focus as well on re-planning, whenever live data collected by the vehicles suggest that the current scenario has deviated from the provisional plan, due to changes in external environmental factors. To this end, we propose a global mission-management architecture, which tries to optimize at once the time required to finish the waste collection, the distance traveled by the vehicles, the amount of fuel burnt (accounting as well for idle time at collection points), and the impact of pollutants emissions.

KEYWORDS: Kerbside Collection, Optimization, Re-Routing, Real-Time, Deep Learning, Scheduling

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